library(dyngen)This vignette demonstrates the different dynamic processes topologies (e.g. bifurcating and cyclic). If you haven’t done so already, first check out the installation instructions in the README.
You can find a full list of backbones using ?list_backbones. This vignette will showcase each of them individually.
backbone <- backbone_linear()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600))
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |============= | 25% elapsed=01s, remaining~03s |========================= | 50% elapsed=01s, remaining~01s |====================================== | 75% elapsed=01s, remaining~00s |==================================================| 100% elapsed=02s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_bifurcating()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600))
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |======== | 14% elapsed=00s, remaining~02s |=============== | 29% elapsed=01s, remaining~02s |====================== | 43% elapsed=01s, remaining~01s |============================= | 57% elapsed=01s, remaining~01s |==================================== | 71% elapsed=01s, remaining~01s |=========================================== | 86% elapsed=02s, remaining~00s |==================================================| 100% elapsed=02s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation likely suffers from bad kinetics; choose a different seed and rerun.
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_bifurcating_converging()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600))
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |======== | 14% elapsed=00s, remaining~01s |=============== | 29% elapsed=00s, remaining~00s |====================== | 43% elapsed=00s, remaining~00s |============================= | 57% elapsed=00s, remaining~00s |==================================== | 71% elapsed=01s, remaining~00s |=========================================== | 86% elapsed=01s, remaining~00s |==================================================| 100% elapsed=01s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_bifurcating_cycle()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600))
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |======= | 12% elapsed=00s, remaining~01s |============= | 25% elapsed=00s, remaining~01s |=================== | 38% elapsed=01s, remaining~01s |========================= | 50% elapsed=01s, remaining~01s |================================ | 62% elapsed=01s, remaining~01s |====================================== | 75% elapsed=01s, remaining~00s |============================================ | 88% elapsed=01s, remaining~00s |==================================================| 100% elapsed=02s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_bifurcating_loop()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600))
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |======== | 14% elapsed=00s, remaining~01s |=============== | 29% elapsed=00s, remaining~01s |====================== | 43% elapsed=00s, remaining~01s |============================= | 57% elapsed=01s, remaining~00s |==================================== | 71% elapsed=01s, remaining~00s |=========================================== | 86% elapsed=01s, remaining~00s |==================================================| 100% elapsed=01s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_binary_tree(
num_modifications = 2
)
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600))
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |===== | 8 % elapsed=00s, remaining~03s |========= | 17% elapsed=01s, remaining~03s |============= | 25% elapsed=01s, remaining~03s |================= | 33% elapsed=01s, remaining~02s |===================== | 42% elapsed=01s, remaining~02s |========================= | 50% elapsed=02s, remaining~02s |============================== | 58% elapsed=02s, remaining~01s |================================== | 67% elapsed=02s, remaining~01s |====================================== | 75% elapsed=02s, remaining~01s |========================================== | 83% elapsed=02s, remaining~00s |============================================== | 92% elapsed=03s, remaining~00s |==================================================| 100% elapsed=03s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation likely suffers from bad kinetics; choose a different seed and rerun.
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_branching(
num_modifications = 2,
min_degree = 3,
max_degree = 3
)
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600))
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |===== | 8 % elapsed=00s, remaining~03s |========= | 17% elapsed=01s, remaining~03s |============= | 25% elapsed=01s, remaining~03s |================= | 33% elapsed=01s, remaining~02s |===================== | 42% elapsed=01s, remaining~02s |========================= | 50% elapsed=02s, remaining~02s |============================== | 58% elapsed=02s, remaining~01s |================================== | 67% elapsed=02s, remaining~01s |====================================== | 75% elapsed=02s, remaining~01s |========================================== | 83% elapsed=02s, remaining~00s |============================================== | 92% elapsed=03s, remaining~00s |==================================================| 100% elapsed=03s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation likely suffers from bad kinetics; choose a different seed and rerun.
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_consecutive_bifurcating()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600))
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |===== | 8 % elapsed=00s, remaining~03s |========= | 17% elapsed=01s, remaining~03s |============= | 25% elapsed=01s, remaining~03s |================= | 33% elapsed=01s, remaining~02s |===================== | 42% elapsed=01s, remaining~02s |========================= | 50% elapsed=02s, remaining~02s |============================== | 58% elapsed=02s, remaining~01s |================================== | 67% elapsed=02s, remaining~01s |====================================== | 75% elapsed=02s, remaining~01s |========================================== | 83% elapsed=02s, remaining~00s |============================================== | 92% elapsed=03s, remaining~00s |==================================================| 100% elapsed=03s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation
#> likely suffers from bad kinetics; choose a different seed and rerun.
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_trifurcating()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |===== | 10% elapsed=00s, remaining~03s |========== | 20% elapsed=01s, remaining~03s |=============== | 30% elapsed=01s, remaining~03s |==================== | 40% elapsed=01s, remaining~02s |========================= | 50% elapsed=01s, remaining~01s |============================== | 60% elapsed=02s, remaining~01s |=================================== | 70% elapsed=02s, remaining~01s |======================================== | 80% elapsed=02s, remaining~01s |============================================= | 90% elapsed=03s, remaining~00s |==================================================| 100% elapsed=03s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_converging()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600))
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |======= | 12% elapsed=00s, remaining~02s |============= | 25% elapsed=00s, remaining~01s |=================== | 38% elapsed=01s, remaining~01s |========================= | 50% elapsed=01s, remaining~01s |================================ | 62% elapsed=01s, remaining~01s |====================================== | 75% elapsed=01s, remaining~00s |============================================ | 88% elapsed=01s, remaining~00s |==================================================| 100% elapsed=02s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_cycle()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600))
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |============= | 25% elapsed=01s, remaining~02s |========================= | 50% elapsed=01s, remaining~01s |====================================== | 75% elapsed=02s, remaining~01s |==================================================| 100% elapsed=03s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_disconnected()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600))
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |==== | 8 % elapsed=00s, remaining~03s |======== | 15% elapsed=00s, remaining~02s |============ | 23% elapsed=01s, remaining~02s |================ | 31% elapsed=01s, remaining~02s |==================== | 38% elapsed=01s, remaining~02s |======================== | 46% elapsed=01s, remaining~01s |=========================== | 54% elapsed=01s, remaining~01s |=============================== | 62% elapsed=02s, remaining~01s |=================================== | 69% elapsed=02s, remaining~01s |======================================= | 77% elapsed=02s, remaining~01s |=========================================== | 85% elapsed=02s, remaining~00s |=============================================== | 92% elapsed=02s, remaining~00s |==================================================| 100% elapsed=03s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation
#> likely suffers from bad kinetics; choose a different seed and rerun.
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_linear_simple()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |========================= | 50% elapsed=00s, remaining~00s |==================================================| 100% elapsed=00s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_cycle_simple()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |============= | 25% elapsed=00s, remaining~01s |========================= | 50% elapsed=00s, remaining~00s |====================================== | 75% elapsed=01s, remaining~00s |==================================================| 100% elapsed=01s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot